The Internet-of-Things (IoT) is a network of interconnected physical objects, such as devices and buildings. The objects include electronics and sensors to collect and exchange data. The Internet-of-Things continuously generates large volumes of data (or data streams), from different types of sensors and electronic devices. The various sensors may monitor different sites with different domain characteristics, e.g., streets, houses, industrial plants and nuclear reactors, each having distinct configuration and reliability requirements. For instance, a temperature increase may be overlooked in a domestic kitchen but it might trigger an alarm in an industrial plant.
When collecting data streams from sensors, information systems are often subject to measurement errors (commonly referred to as outliers) that might be associated with the numerical precision of the equipment, environmental conditions or faulty behavior. It is also common to monitor data streams for specific patterns within the observed series as well as for events of interest within the domain. Such data analysis must typically be tailored to the domain in question.
A need exists for methods and apparatus for processing and managing measurement data. A further need exists for domain-tailored techniques for detection of outliers, patterns and/or events in data streams.